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Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

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Showing 4651–4700 of 17610 papers

TitleStatusHype
Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model—0
CountLLM: Towards Generalizable Repetitive Action Counting via Large Language Model—0
Coupled intrinsic and extrinsic human language resource-based query expansion—0
Coupling Symbolic Reasoning with Language Modeling for Efficient Longitudinal Understanding of Unstructured Electronic Medical Records—0
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization—0
COVID-19 sentiment analysis via deep learning during the rise of novel cases—0
COVID-19 therapy target discovery with context-aware literature mining—0
CoVis: A Collaborative Framework for Fine-grained Graphic Visual Understanding—0
Co-Writing with Opinionated Language Models Affects Users' Views—0
CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology—0
CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment—0
CPP-UT-Bench: Can LLMs Write Complex Unit Tests in C++?—0
CPSDBench: A Large Language Model Evaluation Benchmark and Baseline for Chinese Public Security Domain—0
CPS-LLM: Large Language Model based Safe Usage Plan Generator for Human-in-the-Loop Human-in-the-Plant Cyber-Physical System—0
CPTQuant -- A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models—0
CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis—0
Crawling the Internal Knowledge-Base of Language Models—0
Creating a Large Language Model of a Philosopher—0
Creating and using large monolingual parallel corpora for sentential paraphrase generation—0
Creating Arabic LLM Prompts at Scale—0
Creating dialect sub-corpora by clustering: a case in Japanese for an adaptive method—0
Creating Domain-Specific Translation Memories for Machine Translation Fine-tuning: The TRENCARD Bilingual Cardiology Corpus—0
Creating Large Language Model Resistant Exams: Guidelines and Strategies—0
Creating Lithuanian and Latvian Speech Corpora from Inaccurately Annotated Web Data—0
Creativity in LLM-based Multi-Agent Systems: A Survey—0
Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending—0
CREFT: Sequential Multi-Agent LLM for Character Relation Extraction—0
Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination—0
Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training—0
Critical Data Size of Language Models from a Grokking Perspective—0
Critical Phase Transition in Large Language Models—0
Critic-CoT: Boosting the reasoning abilities of large language model via Chain-of-thoughts Critic—0
Croatian Dependency Treebank: Recent Development and Initial Experiments—0
CROME: Cross-Modal Adapters for Efficient Multimodal LLM—0
Croppable Knowledge Graph Embedding—0
Cropper: Vision-Language Model for Image Cropping through In-Context Learning—0
Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers—0
Cross-composition Feature Disentanglement for Compositional Zero-shot Learning—0
Cross-Domain Language Modeling: An Empirical Investigation—0
Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation—0
Cross-Examiner: Evaluating Consistency of Large Language Model-Generated Explanations—0
Crossing New Frontiers: Knowledge-Augmented Large Language Model Prompting for Zero-Shot Text-Based De Novo Molecule Design—0
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure—0
Cross-Lingual Transfer Learning for Statistical Type Inference—0
Cross-lingual Transfer of Abstractive Summarizer to Less-resource Language—0
Cross-lingual Character-Level Neural Morphological Tagging—0
Cross-lingual Universal Dependency Parsing Only from One Monolingual Treebank—0
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering—0
Cross-Lingual Language Modeling with Syntactic Reordering for Low-Resource Speech Recognition—0
Cross-Lingual Language Model Meta-Pretraining—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified